Neoplasm Grading

In order to explore the geographic patterns of our two outcomes of interest, we used the spatial scan statistic [15 ] to detect and evaluate the statistical significance of any geographic clusters of each outcome. This method imposes a very large number of overlapping circles of different location and size on the map, each of which is a potential cluster, and adjusts for the multiple testing inherent in the many circles considered.
Our cluster detection method identified clusters of both high and low rates, with a maximum scanning window size to include up to 50% of the population at risk. Secondary clusters were reported if they had no geographic overlap with more likely clusters. P-values were derived from 999 simulated Monte Carlo replications under the null hypothesis of spatial randomness of outcomes of interest.
We conducted three separate cluster detection analyses for each of the two prostate cancer outcomes: higher histologic grade of tumor, and later stage at diagnosis. In the unadjusted analysis, under the null hypothesis, the expected number of more aggressive grade or late stage cases in a block group was calculated by multiplying the total case population of the block group by the statewide rate of the outcome of interest. Thus, in the unadjusted analysis, a block group would be expected to have the same rate or proportion of late stage or high grade cases in its case population as the State. In the two adjusted analyses, the expected number of aggressive grade or later stage cases was calculated from a regression model containing individual case characteristics, or from a regression model with both individual and area-level covariates. Based on the expected counts, the number of aggressive grade and later stage cases in each block group was modeled as a Poisson distribution.
For the unadjusted analyses, we also used a Bernoulli model to compare the distribution of so-called "cases" (those with aggressive grade or late stage) to "controls" (less aggressive grade or early stage) based on point location of each residential address, rather than rates within block groups. This was useful to compare the sensitivity of the Poisson model assumption for aggregated data to that of the unaggregated Bernoulli method. No major differences in results were found, and to allow proper comparison between the adjusted and unadjusted analyses, the Poisson model results are presented for all three types of analyses.
For each cluster identified, we list the radius, number of block groups in the cluster, the observed versus expected number of late stage or aggressive grade cases, the relative risk and the p value. The relative risk is the risk of the respective outcome within the cluster, compared to the population's risk. We report clusters with statistical significance p < .05 that do not overlap with another reported cluster with a lower p-value. Calculations were done using the freely available SaTScan v4.0 software .

Archival specimens used for tissue microarray (TMA) were obtained from 398 patients at Liver Cancer Institute, Zhongshan Hospital, Fudan University between 2006 and 2008. TMA were constructed by Shanghai Outdo Biotech Company. The inclusion and exclusion criteria of the patient cohorts include (a) having a distinctive pathologic diagnosis of HCC, (b) having no anticancer treatment before liver resection, (c) having curative liver resection, (d) having suitable formalin-fixed, paraffin-embedded tissues or frozen tissues, and (e) having a complete clinicopathologic and follow-up data. The patient information was listed in Table 3. Ethical approval for human subjects was obtained from the research ethics committee of Zhongshan Hospital; informed consent was obtained from each patient.
Curative resection was defined as complete resection of all tumor nodules and the cut surface being free of cancer by histologic examination; having no cancerous thrombus in the portal vein (main trunk or two major branches), hepatic veins, or bile duct; and having no extrahepatic metastasis37 (link). The staging of tumors was determined according to the TNM classification system of the 7th edition. The histological grade of tumor differentiation was assigned by the Edmondson grading system38 .

This single-center, retrospective study was conducted at the Department of Gastroenterological Surgery, University of Health Sciences Kosuyolu High Specialization Education and Research Hospital, Istanbul, Turkey. The study was carried out in accordance with the Helsinki Declaration and local laws and regulations. This study was approved by the ethical committee of Kosuyolu High Specialization Education and Research Hospital with an IRB number: 2020/14/404.
Between December 2006 and December 2019, medical records of 324 patients who underwent gastric cancer surgery were retrospectively reviewed and data of 163 eligible patients were enrolled in the study (Figure 1). Patients aged over 18 who underwent a curative surgery for TNM stage II or III GC were considered eligible for this study. All patients underwent open total or subtotal gastrectomy with D2 lymphadenectomy. Patients who underwent emergency surgery, had immunodeficiency or lymphoproliferative disease and had taken immunomodulatory drugs were excluded. Also, patients whose adjuvant chemotherapy was not completed were not included into the study.
Data regarding the patients' age, gender, comorbidity status, presence/absence of lymphovascular and perineural invasions (LVI and PNI), tumor histological grade, tumor size and location, total number of harvested LNs and metastatic LNs, size of the largest MLN, length of hospital stay, postoperative complications, overall survival (OS), neoadjuvant treatment status were recorded. The Clavien-Dindo classification was used to analyze postoperative complications, and grade III or higher complications were defined as major complications (11 ).
Adjuvant chemotherapy was given to all patients with a pathological stage II and III gastric cancer with LN metastases. DCF (Docetaxel, cisplatin, 5-fluorouracil) or FLOT (5-fluorouracil, leucovorin, oxaliplatin, docetaxel) regimens were given as both neoadjuvant and adjuvant chemotherapy.
The software IBM® SPSS® (Statistical Package for the Social Sciences) version 23 (IBM Corp. Armonk, NY, USA) was used for statistical analysis. Qualitative data were presented as frequency and percentage. The distribution of numerical data was performed using the Kolmogorov–Smirnov test with the non-normal distribution results. Quantitative data were given as median with Interquartile Range (IQR). The association of major complications and survival with categorical variables was analyzed using Chi-square, Fisher's exact tests, and Likelihood ratio. The Mann–Whitney-U test was used to examine whether major complications and survival were related to age, metastatic lymph node size, and length of hospital stay. The Kaplan–Meier method and the log-rank test were used to conduct the survival analyses of the metastatic lymph node size. Further, multivariate Cox regression analyses were performed to examine role of the metastatic lymph node size in predicting mortality. A p-value of less than 0.05 was defined as statistically significant.

A single-center study was carried out in a sample of 61 patients with bladder cancer and control subjects (n = 50). Participants were recruited from the Clinical Department of Urology and Urology Oncology of the Municipal Hospital in Rzeszow, Poland, from February to August 2022.
Inclusion criteria: the study involved newly diagnosed bladder cancer with a confirmed diagnosis based on histopathological evaluation of samples from TURBT or radical cystectomy. Histopathological evaluations of frozen sections were consistent with a urothelial bladder tumor. Patients with bladder cancer regardless of tumor grade in stage Ta-T2 were eligible for the study; patients in T3-T4 with increased uncertainty of prediction were excluded from the study. TURBTs were performed under regional anesthesia, while radical cystectomy was performed under general anesthesia using a laparoscopic transperitoneal approach. Due to the clinical stage, the participants were divided according to the TNM classification [17 ]. Moreover, the histological grade of the tumor was defined according to the WHO grade of 1973 [18 (link)].
Exclusion criteria included: a history of endocrine disorders and other malignant diseases, diabetes mellitus, dyslipidemia, obesity (BMI > 30), infectious or inflammatory diseases, and failure to sign informed consent by the patient. A flow chart outlining the recruitment process is shown in Figure 1.
Healthy controls were recruited among subjects who came to the local clinic to have check-ups at the same time. The control group consisted of volunteers with no history of cancer or chronic inflammation (inclusion criteria: blood counts and biochemical blood tests within the reference values; no use of any antioxidant vitamins). Control participants did not take any medication 30 days prior the study. Healthy controls had normal urinary function tests. All participants had similar socioeconomic status and similar food preferences. According to the interview conducted among the study participants, 70% of them are smokers (both in the patients and controls). Anthropometric measurements were also obtained from the participants, including height and weight. The BMI was calculated as kg/m².

The TCGA-KIRP dataset (n = 256) was chosen to verify our IRSS model and assess the generalization of the signature since KIRC and KIRP tumors are anatomically and histologically quite similar. Every patient’s risk score in the KIRP cohort was computed and ranked using the IRSS formula that we developed using the TCGA-KIRC dataset. The TCGA-KIRP cohort’s cutoff values were used to classify the KIRP samples into groups with high- and low-risk as per the scores. The KM curve was used to compare survival across the two groups. The ROC curve was used to evaluate the precision of the signature prediction. In addition, the nomogram was used to evaluate the survival probabilities of the KIRP patients using clinicopathological data, including age, gender, histologic grade of the tumor, pathologic stage of the tumor, and the IRSS.